[Benchmark] Add all gather matmul benchmark

[joydddd]

Stacked PRs:

• ->[Benchmark] Add all gather matmul benchmark #400
• [Benchmark] Add all reduce benchmark #393

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[Benchmark] Add all gather matmul benchmark

[joydddd]

shape	dtype	nccl	torch_symm_mem	helion	kraken	Best Backend
(256, 256, 256)	torch.bfloat16	41.156	431.319	119.549	111.359	nccl
(384, 384, 384)	torch.bfloat16	42.737	424.313	137.685	105.681	nccl
(512, 512, 512)	torch.bfloat16	51.284	952.367	147.106	180.390	nccl
(640, 640, 640)	torch.bfloat16	54.416	946.200	124.764	nan	nccl
(768, 768, 768)	torch.bfloat16	2373.010	467.570	127.340	nan	helion
(896, 896, 896)	torch.bfloat16	79.772	450.538	230.704	nan	nccl
(1024, 1024, 1024)	torch.bfloat16	100.879	628.553	144.646	161.596	nccl
(1152, 1152, 1152)	torch.bfloat16	122.012	628.340	164.184	nan	nccl
(1280, 1280, 1280)	torch.bfloat16	159.020	433.261	183.333	nan	nccl
(1408, 1408, 1408)	torch.bfloat16	194.298	433.295	196.417	nan	nccl
(1536, 1536, 1536)	torch.bfloat16	211.553	431.485	206.027	297.015	helion
(1664, 1664, 1664)	torch.bfloat16	251.526	427.265	581.858	nan	nccl
(1792, 1792, 1792)	torch.bfloat16	286.406	678.410	246.517	nan	helion
(1920, 1920, 1920)	torch.bfloat16	341.697	974.870	264.127	nan	helion
(2048, 2048, 2048)	torch.bfloat16	380.024	446.875	287.984	481.179	helion
(2176, 2176, 2176)	torch.bfloat16	445.310	477.962	333.809	nan	helion
(2304, 2304, 2304)	torch.bfloat16	496.317	457.464	363.813	nan	helion
(2432, 2432, 2432)	torch.bfloat16	597.861	460.951	397.363	nan	helion
(2560, 2560, 2560)	torch.bfloat16	655.093	489.344	430.963	804.186	helion
(2688, 2688, 2688)	torch.bfloat16	775.004	1021.574	1146.624	nan	nccl
(2816, 2816, 2816)	torch.bfloat16	839.021	562.788	691.636	nan	torch_symm_mem

shape	dtype	nccl	torch_symm_mem	helion	kraken	Best Backend
(2944, 2944, 2944)	torch.bfloat16	973.901	625.908	649.289	nan	torch_symm_mem
(3072, 3072, 3072)	torch.bfloat16	1012.294	680.064	737.418	722.340	torch_symm_mem
(3200, 3200, 3200)	torch.bfloat16	1458.743	1001.001	927.776	nan	helion
(3328, 3328, 3328)	torch.bfloat16	1340.722	961.890	2509.657	nan	torch_symm_mem
(3456, 3456, 3456)	torch.bfloat16	2240.075	2239.796	1171.304	nan	helion
(3584, 3584, 3584)	torch.bfloat16	2181.338	2044.006	1729.832	1456.677	kraken
(3712, 3712, 3712)	torch.bfloat16	4645.679	3967.047	2498.389	nan	helion
(3840, 3840, 3840)	torch.bfloat16	1950.385	1464.106	1650.844	nan	torch_symm_mem
(3968, 3968, 3968)	torch.bfloat16	2937.086	2874.260	1747.495	nan	helion
(4096, 4096, 4096)	torch.bfloat16	2819.741	6313.106	1725.565	1867.402	helion

[joydddd]

Optimization implemented in Kraken but not supported in Helion:

(a, out) = ag_matmul(a_shared, b), where a = all_gather(a_shared), and out = a@b.
For an a tile originated from the local a_shared, there's potential timesaving by accessing it directly through a_shared, and skip waiting for cudaMemcpy.

Helion does not support conditional calculate tile offset and conditionally use different tensor_descriptor for tensor_descriptor.load. i.e.

if xx: 
   a_load_desc = a_shared_desc
   a_load_offset = a_shared_stride_0 * .... 
else: 
   a_load_desc = a_desc
   a_load_offset = a_stride_0 * .... 
a_load_desc.load(a_load_offset) 

Same access pattern can be implementation in Helion as:

if xx: 
    a_tile = a_shared[tile.index - RANK * M_per_RANK]
else: 
    a_tile = a[tile]

But this generates 2 tensor_descriptor loads in each branch, and breaks Triton data prefetching.

[jansel]

If xx is a constexpr would that fix it too? Can it be in this case?

[joydddd]

If xx is a constexpr would that fix it too? Can it be in this case?

Yep. If xx is a constexpr the software pipelining should be fine.
In this case however, xx is dependent on the loop iterator. In most cases the loop is too large to unroll (via tl.static_range) :(